Principal Engineer, AI & Data Platform

Skills

About the Role

You will own the architecture and delivery of systems that make data accessible and intelligible to business users and AI agents. You will design and implement the enterprise knowledge layer including semantic models and knowledge graphs, lead the strategy for conversational analytics, build an enterprise data platform with tool connectivity and integration with BigQuery Graph and Knowledge Catalog, drive adoption of graph databases and hybrid query engines, partner with domain stakeholders across trading and compliance to ensure the data platform serves the business, establish evaluation frameworks for AI systems to ensure groundedness and factual reliability, and set architectural direction while mentoring engineers to deliver high quality scalable systems.

Requirements

• 7+ years in data engineering analytics or AI platform roles
• Demonstrated experience building and operating enterprise scale data platforms in production
• Direct experience building natural language query systems over structured data
• Hands on experience with graph databases and knowledge graphs or ontology driven data architectures
• Experience with at least three of the following: graph databases and query languages (Neo4j TigerGraph Amazon Neptune or BigQuery Graph); knowledge graph construction and ontology modeling (RDF OWL property graphs taxonomy design); GraphRAG architectures; semantic layer and BI platforms (Looker dbt Semantic Layer AtScale); vector databases and hybrid retrieval (Qdrant Pinecone pgvector AlloyDB vector search); cloud data platforms at scale (BigQuery Snowflake Databricks Spanner); data cataloging and governance (Google Knowledge Catalog Dataplex Collibra Alation Atlan); MCP for agent data connectivity
• Agent and AI systems expertise including evaluation methodology for AI systems
• Cloud infrastructure with strong GCP experience including BigQuery Vertex AI Dataplex Knowledge Catalog
• Engineering rigor with CI CD for data and ML pipelines and infrastructure as code practices
• Excellent communication and ability to influence and present to senior stakeholders

Responsibilities

• Design and own the enterprise knowledge layer including semantic models and knowledge graphs
• Lead the strategy and delivery of natural language interfaces to business data
• Architect the infrastructure that enables AI agents to discover reason over and act on enterprise data
• Drive adoption of graph databases and hybrid query engines for multi hop reasoning and analytics
• Partner with domain stakeholders to ensure the data platform serves the business and govern the semantic layer
• Establish evaluation frameworks for AI systems to ensure groundedness and factual reliability
• Set architectural direction mentor engineers and represent the team’s technical vision to senior stakeholders